VacationPy¶

Starter Code to Import Libraries and Load the Weather and Coordinates Data¶

In [1]:
# Dependencies and Setup
import hvplot.pandas
import pandas as pd
import requests
import numpy as np
import json

# https://pypi.org/project/geoviews/:   conda install -c pyviz geoviews-core 
# pip install pyproj
import cartopy.crs as ccrs
import geoviews as gv

# Import API key
from api_keys import geoapify_key
In [2]:
# Load the CSV file created in Part 1 into a Pandas DataFrame
city_data_df = pd.read_csv("output_data/cities.csv")
# city_data_df = pd.read_csv("output_data/clean_city_data.csv")

# Display sample data
city_data_df.head()
Out[2]:
City_ID City Lat Lng Max Temp Humidity Cloudiness Wind Speed Country Date
0 0 east london -33.0153 27.9116 67.14 63.0 75.0 4.61 ZA 1.675011e+09
1 1 cherskiy 68.7500 161.3000 -14.40 98.0 77.0 1.77 RU 1.675011e+09
2 2 kodiak 57.7900 -152.4072 35.11 100.0 100.0 0.00 US 1.675011e+09
3 3 santa cruz -17.8000 -63.1667 82.45 74.0 75.0 14.97 BO 1.675011e+09
4 4 rikitea -23.1203 -134.9692 78.55 74.0 77.0 10.87 PF 1.675011e+09

Step 1: Create a map that displays a point for every city in the city_data_df DataFrame. The size of the point should be the humidity in each city.¶

In [3]:
%%capture --no-display


# Configure the map plot
map_plot_3 = city_data_df.hvplot.points(
    'Lng',
    'Lat',
    geo = True,
    tiles = "OSM",
    frame_width = 700,
    frame_height = 500,
    size = "Humidity",
    scale = 1,
    color = "City"
)

# Display the map
map_plot_3
Out[3]:

Step 2: Narrow down the city_data_df DataFrame to find your ideal weather condition¶

In [9]:
# Narrow down cities that fit criteria and drop any results with null values
# Drop any rows with null values
clean_cities_df = city_data_df.dropna(how="any")
# print(clean_cities_df.count())
clean_cities_df = clean_cities_df[ 
    (clean_cities_df['Max Temp'] > 18) & (clean_cities_df['Max Temp'] < 25) &
    (clean_cities_df['Wind Speed'] < 5)
]
# Display sample data
clean_cities_df
Out[9]:
City_ID City Lat Lng Max Temp Humidity Cloudiness Wind Speed Country Date
318 351 port hardy 50.6996 -127.4199 24.98 93.0 20.0 4.61 CA 1.675011e+09
362 397 shitanjing 39.2342 106.3439 23.00 24.0 0.0 2.84 CN 1.675011e+09
363 398 zhaoyuan 37.3592 120.3964 24.85 70.0 2.0 3.31 CN 1.675011e+09
486 534 yumen 40.2833 97.2000 21.79 26.0 86.0 4.56 CN 1.675011e+09
511 561 kars 40.6020 43.0950 21.65 93.0 100.0 0.00 TR 1.675011e+09
548 598 hami 42.8000 93.4500 20.25 32.0 97.0 2.98 CN 1.675011e+09

Step 3: Create a new DataFrame called hotel_df.¶

In [10]:
# Use the Pandas copy function to create DataFrame called hotel_df to store the city, country, coordinates, and humidity
hotel_df = clean_cities_df[["City","Country","Lat","Lng","Humidity"]].copy()
# Add an empty column, "Hotel Name," to the DataFrame so you can store the hotel found using the Geoapify API
hotel_df['Hotel Name'] = np.nan
# Display sample data
hotel_df
Out[10]:
City Country Lat Lng Humidity Hotel Name
318 port hardy CA 50.6996 -127.4199 93.0 NaN
362 shitanjing CN 39.2342 106.3439 24.0 NaN
363 zhaoyuan CN 37.3592 120.3964 70.0 NaN
486 yumen CN 40.2833 97.2000 26.0 NaN
511 kars TR 40.6020 43.0950 93.0 NaN
548 hami CN 42.8000 93.4500 32.0 NaN

Step 4: For each city, use the Geoapify API to find the first hotel located within 10,000 metres of your coordinates.¶

In [11]:
# Set parameters to search for a hotel
radius = 10 * 1000

## I don't know something wrong, geoapify_key cannot be obtained from api_keys.py file 
# params = {
#     "radius": radius,
#     "type": "accommodation.hotel",
#     "apiKey": geoapify_key
# }

# Therefore, I put geoapify_key's value here
params = {
    "radius": radius,
    "type": "accommodation.hotel",
    "apiKey": "aa46f49c727b4be1aeb53b12259f3e6a"
}

# Print a message to follow up the hotel search
print("*********** Starting hotel search ***********")

# Iterate through the hotel_df DataFrame
for index, row in hotel_df.iterrows():
    # get latitude, longitude from the DataFrame
    lat = row['Lat']
    lng = row['Lng']
    
    # Add filter and bias parameters with the current city's latitude and longitude to the params dictionary
    params["filter"] = f"circle:{lng},{lat},{radius}"
    params["bias"] = f"proximity:{lng},{lat}"
    
    # Set base URL
    base_url = "https://api.geoapify.com/v2/places"


    # Make and API request using the params dictionaty
    name_address = requests.get(base_url, params=params)
    
    # Convert the API response to JSON format
    name_address = name_address.json()
    
    # Grab the first hotel from the results and store the name in the hotel_df DataFrame
    try:
        hotel_df.loc[index, "Hotel Name"] = name_address["features"][0]["properties"]["address_line1"]
        
#         print(json.dumps(name_address, indent=4, sort_keys=True))
        
    except (KeyError, IndexError):
        # If no hotel is found, set the hotel name as "No hotel found".
        hotel_df.loc[index, "Hotel Name"] = "No hotel found"
        
    # Log the search results
    print(f"{hotel_df.loc[index, 'City']} - nearest hotel: {hotel_df.loc[index, 'Hotel Name']}")

print("*********** Complete searching hotel ***********")

# Display sample data
hotel_df

*********** Starting hotel search ***********
port hardy - nearest hotel: Quarterdeck Inn
shitanjing - nearest hotel: No hotel found
zhaoyuan - nearest hotel: No hotel found
yumen - nearest hotel: No hotel found
kars - nearest hotel: Grand Ani Hotel
hami - nearest hotel: Hami Hotel 2nd Bldg
*********** Complete searching hotel ***********
Out[11]:
City Country Lat Lng Humidity Hotel Name
318 port hardy CA 50.6996 -127.4199 93.0 Quarterdeck Inn
362 shitanjing CN 39.2342 106.3439 24.0 No hotel found
363 zhaoyuan CN 37.3592 120.3964 70.0 No hotel found
486 yumen CN 40.2833 97.2000 26.0 No hotel found
511 kars TR 40.6020 43.0950 93.0 Grand Ani Hotel
548 hami CN 42.8000 93.4500 32.0 Hami Hotel 2nd Bldg

Step 5: Add the hotel name and the country as additional information in the hover message for each city in the map.¶

In [12]:
%%capture --no-display

# Configure the map plot

map_plot_3 = hotel_df.hvplot.points(
    'Lng',
    'Lat',
    geo = True,
    tiles = "OSM",
    frame_width = 700,
    frame_height = 500,
    scale = 1,
    color = "City",
    hover_cols = ["Hotel Name", "Country"]
)

# Display the map
map_plot_3
Out[12]:
In [ ]: